A Pre-game Item Recommendation Method Based on Self-Supervised Learning

With the development of e-sports, Multiplayer Online Battle Arena (MOBA) has become the most popular game type in the world. In addition to bringing huge commercial value, it also produces a large amount of rich public game data, which attracts researchers to deeply combine deep learning technology with MOBA. Among them, item recommendation is the core task, including pre-game item recommendation and in-game item recommendation. Compared with in-game item recommendation, pre-game recommendation faces serious data sparsity and missing data labels, which makes pre-game item recommendation a hard challenge. Existing methods pay more attention to conventional indicators such as precision and recall, but ignore the usability of results in MOBA, and have not fully considered the relationship between items. In this paper, we propose a pre-game item recommendation method based on self-supervised learning. While repairing the missing labels, the Transformer encoder is used to learn the relationship between items. Experimental results on a public dataset show that our method outperforms previous methods in terms of precision and usability indicators.